knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
library(codebook)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
#codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data <- rio::import("online_data.csv")
#drop <- c("V1", "submitdate", "startlanguage", "click")
#codebook_data = codebook_data[ , !(names(codebook_data) %in% drop)]
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)
var_label(codebook_data) <- list(
  id = "ID variable from raw data", 
  lastpage = "Last page completed by the participant, page 12 and 13 are considered as full participation", 
  random = "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.", 
  cb = "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details", 
  ca = "Data sharing policy presented", 
  mc_1 = "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?", 
  mc_2 = "comprehension question consent 2 (distractor): Is your data anonymous?", 
  mc_3 = "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?", 
  mc_4 = "comprehension question consent 3 (distractor): Can you stop your participation at any time?", 
  bf_1 = "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.", 
  bf_2 = "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.", 
  bf_3 = "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.", 
  cr_1 = "Careless response item 1: I am using an electronic device at this moment.", 
  bf_5 = "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.", 
  bf_6 = "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.", 
  bf_7 = "TIPI item 6, Extraversion: I see myself as reserved, quiet.", 
  bf_8 = "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.", 
  cr_2 = "Careless response item 2: I turn into a leprechaun at night.", 
  bf_10 = "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.", 
  bf_11 = "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.", 
  bf_12 = "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.", 
  soc_d_1 = "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
  soc_d_2 = "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.", 
  soc_d_3 = "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.", 
  soc_d_4 = "Social desirability questionnaire item 4: I have never intensely disliked anyone.", 
  soc_d_5 = "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.", 
  soc_d_6 = "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.", 
  cr_3 = "Careless response item 3: All my friends are aliens.", 
  soc_d_7 = "Social desirability questionnaire item 7: I am always careful about my manner of dress.", 
  soc_d_8 = "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.", 
  soc_d_9 = "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.", 
  soc_d_10 = "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.", 
  soc_d_11 = "Social desirability questionnaire item 11: I like to gossip at times.", 
  soc_d_12 = "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.", 
  cr_4 = "Careless response item 4: All my friends say I would make a great poodle.", 
  soc_d_13 = "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.", 
  soc_d_14 = "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.", 
  soc_d_15 = "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.", 
  soc_d_16 = "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.", 
  soc_d_17 = "Social desirability questionnaire item 17: I always try to practice what I preach.", 
  soc_d_18 = "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.", 
  cr_5 = "Careless response item 5: I eat cement occasionally.", 
  soc_d_19 = "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.", 
  soc_d_20 = "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.", 
  soc_d_21 = "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.", 
  soc_d_22 = "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.", 
  soc_d_23 = "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.", 
  soc_d_24 = "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.", 
  soc_d_25 = "Social desirability questionnaire item 25: I never resent being asked to return a favor.", 
  soc_d_26 = "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.", 
  cr_6 = "Careless response item 6: I can teleport across time and space.", 
  soc_d_27 = "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.", 
  soc_d_28 = "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.", 
  soc_d_29 = "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.", 
  soc_d_30 = "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ", 
  soc_d_31 = "Social desirability questionnaire item 31: I have never felt that I was punished without cause.", 
  cr_7 = "Careless response item 7: I will be punished for meeting the requirements of my job.", 
  soc_d_32 = "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.", 
  soc_d_33 = "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.", 
  everesthigh = "Anchoring paradigm, high anchor: Height of Mount Everest", 
  chicagohigh = "Anchoring paradigm, high anchor: Population of Chicago", 
  babieshigh = "Anchoring paradigm, high anchor: Babies born each day", 
  everestlow = "Anchoring paradigm, low anchor: Height of Mount Everest", 
  chicagolow = "Anchoring paradigm, low anchor: Population of Chicago", 
  babieslow = "Anchoring paradgim, low anchor: Babies born each day", 
  d1 = "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?", 
  d2.sq001 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember", 
  d2.sq002 = "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember", 
  d3.sq001 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes
", 
  d3.sq002 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No", 
  d3.sq003 = "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember", 
  gender = "Gender: open-entry self-report", 
  age = "Age categories", 
  consent = "Variables cb and ca combined in one variable", 
  cond_anc = "Anchoring condition: high and low", 
  refused = "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.", 
  remember = "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?", 
  anc_baby = "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable", 
  anc_everest = "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable", 
  anc_chicago = "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable", 
  gender_r = "Gender variable cleaned for grammar, language variations and orthography"
       )

val_labels(codebook_data$remember) <- c("No or wrong memory" = 0, "correct memory" = 1)

add_likert_labels <- function(x) {
   val_labels(x) <- c("No" = 0, 
                      "Yes" = 1)
   x
 }
likert_items <- names(codebook_data[, c("soc_d_1", "soc_d_2", "soc_d_3", "soc_d_4", "soc_d_5", "soc_d_6", "soc_d_7", "soc_d_8", "soc_d_9", "soc_d_10", "soc_d_11", "soc_d_12", "soc_d_13", "soc_d_14", "soc_d_15", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19", "soc_d_20", "soc_d_21", "soc_d_22", "soc_d_23", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28", "soc_d_29", "soc_d_30", "soc_d_31", "soc_d_32", "soc_d_33") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

add_likert_labels <- function(x) {
   val_labels(x) <- c("Yes" = 0, 
                      "No" = 1)
   x
 }
likert_items <- names(codebook_data[, c("cr_1", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)


add_likert_labels <- function(x) {
   val_labels(x) <- c("Disagree strongly" = 1,
                      "Disagree moderately" = 2,
                      "Disagree a little" = 3,
                      "Neither agree nor disagree" = 4,
                      "Agree a little" = 5,
                      "Agree moderately" = 6,
                      "Agree strongly" = 7) 
   x
 }
likert_items <- names(codebook_data[, c("bf_1", "bf_2",
                                        "bf_3", "bf_5", "bf_6",
                                        "bf_7", "bf_8", "bf_10", 
                                        "bf_11", "bf_12") ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

####     Extraversion     ####
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7") %>% aggregate_and_document_scale()

reversed_items <- c("bf_7")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_7R") %>% aggregate_and_document_scale()

####    Agreeableness    ####
codebook_data$Agreeableness <- codebook_data %>% select("bf_2", "bf_8") %>% aggregate_and_document_scale()

reversed_items <- c("bf_2")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Agreeableness <- codebook_data %>% select("bf_2R", "bf_8") %>% aggregate_and_document_scale()

####    Conscientiousness    ####

codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10") %>% aggregate_and_document_scale()

reversed_items <- c("bf_10")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_10R") %>% aggregate_and_document_scale()

####    Neuroticism    ####
codebook_data$Neuroticism <- codebook_data %>% select("bf_5", "bf_11") %>% aggregate_and_document_scale()

reversed_items <- c("bf_5")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Neuroticism <- codebook_data %>% select("bf_5R", "bf_11") %>% aggregate_and_document_scale()

####    Openness to experience    ####

codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12") %>% aggregate_and_document_scale()

reversed_items <- c("bf_12")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_6", "bf_12R") %>% aggregate_and_document_scale()

####    Social Desirability    ####
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3", 
         "soc_d_4", "soc_d_5", "soc_d_6", 
         "soc_d_7", "soc_d_8", "soc_d_9", 
         "soc_d_10", "soc_d_11", "soc_d_12", 
         "soc_d_13", "soc_d_14", "soc_d_15", 
         "soc_d_16", "soc_d_17", "soc_d_18", 
         "soc_d_19", "soc_d_20", "soc_d_21", 
         "soc_d_22", "soc_d_23", "soc_d_24", 
         "soc_d_25", "soc_d_26", "soc_d_27", 
         "soc_d_28", "soc_d_29", "soc_d_30", 
         "soc_d_31", "soc_d_32", "soc_d_33") %>% aggregate_and_document_scale()

reversed_items <- c("soc_d_3", "soc_d_5", "soc_d_6", "soc_d_9",
                    "soc_d_10", "soc_d_11","soc_d_12","soc_d_14",
                    "soc_d_15", "soc_d_19", "soc_d_22", "soc_d_23",
                    "soc_d_28", "soc_d_30", "soc_d_32"
                    )
codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Social Desirability' <- codebook_data %>% select("soc_d_1", "soc_d_2", "soc_d_3R", 
         "soc_d_4", "soc_d_5R", "soc_d_6R", 
         "soc_d_7", "soc_d_8", "soc_d_9R", 
         "soc_d_10R", "soc_d_11R", "soc_d_12R", 
         "soc_d_13", "soc_d_14R", "soc_d_15R", 
         "soc_d_16", "soc_d_17", "soc_d_18", 
         "soc_d_19R", "soc_d_20", "soc_d_21", 
         "soc_d_22R", "soc_d_23R", "soc_d_24", 
         "soc_d_25", "soc_d_26", "soc_d_27", 
         "soc_d_28R", "soc_d_29", "soc_d_30R", 
         "soc_d_31", "soc_d_32R", "soc_d_33") %>% aggregate_and_document_scale()

codebook_data$'Careless responses' <- codebook_data %>% select("cr_1", "cr_2", "cr_3", 
         "cr_4", "cr_5", "cr_6", 
         "cr_7") %>% aggregate_and_document_scale()
reversed_items <- c("cr_1")
codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Careless responses' <- codebook_data %>% select("cr_1R", "cr_2", "cr_3", 
         "cr_4", "cr_5", "cr_6", 
         "cr_7") %>% aggregate_and_document_scale()


metadata(codebook_data)$name <- "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, public data set"
metadata(codebook_data)$description <- "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared in its entirety, as the study consent stated to half the participants that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)"
metadata(codebook_data)$identifier <- "https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$creator <- "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein"
metadata(codebook_data)$citation <- "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC"
metadata(codebook_data)$datePublished <- "2019-08-06"
metadata(codebook_data)$temporalCoverage <- "2019-06-17 to 2019-06-21" 
metadata(codebook_data)$spatialCoverage <- "Online participants residing in, or citizens of, the EU at time of data collection" 
#rio::export(codebook_data, "offline_data_shared.rds")

codebook(codebook_data)
knitr::asis_output(data_info)

Metadata

Description

if (exists("name", meta)) {
  glue::glue(
    "__Dataset name__: {name}",
    .envir = meta)
}

Dataset name: Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, public data set

cat(description)

10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared in its entirety, as the study consent stated to half the participants that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)

Metadata for search engines

  • Temporal Coverage: 2019-06-17 to 2019-06-21
  • Spatial Coverage: Online participants residing in, or citizens of, the EU at time of data collection
  • Citation: Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC

  • Identifier: https://doi.org/10.17605/OSF.IO/AM6BC
  • Date published: 2019-08-06

  • Creator:Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein

meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
pander::pander(meta)
  • keywords: V1, id, consent, cond_anc, refused, remember, anc_baby, anc_everest, anc_chicago, gender_r, oq, bf_1, bf_2R, bf_3, bf_5R, bf_6, bf_7R, bf_8, bf_10R, bf_11, bf_12R, soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33, cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7, everesthigh, chicagohigh, babieshigh, everestlow, chicagolow, babieslow, d1, d2.sq001, d2.sq002, d3.sq001, d3.sq002, d3.sq003, gender, age, end, return, lastpage, random, cb, ca, mc_1, mc_2, mc_3, mc_4, Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to experience, Social Desirability and Careless responses

knitr::asis_output(survey_overview)

Variables

if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

V1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
V1 integer 0 576 576 288.5 166.42 1 144.75 288.5 432.25 576 ▇▇▇▇▇▇▇▇
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

id

ID variable from raw data

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
id ID variable from raw data integer 0 576 576 318.87 187.35 1 156.75 317.5 480.25 654 ▇▇▇▇▇▇▇▆
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

cond_anc

Anchoring condition: high and low

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
cond_anc Anchoring condition: high and low character 0 576 576 0 2 1 1
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

refused

Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition.

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
refused Refusal to participate, one participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. This participant was in the ‘no data sharing’ condition. integer 0 576 576 0.0017 0.042 0 0 0 0 1 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

remember

Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
remember Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy? integer 0. No or wrong memory,
1. correct memory
1 575 576 0.55 0.5 0 0 1 1 1 ▆▁▁▁▁▁▁▇
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}
  • No or wrong memory: 0
  • correct memory: 1

anc_baby

Aggregated anchoring response, combining variables babieshigh and babieslow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

3 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
anc_baby Aggregated anchoring response, combining variables babieshigh and babieslow in one variable numeric 3 573 576 93485.25 444398.92 1.8 1000 10000 40000 4e+06 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_everest

Aggregated anchoring response, combining variables everesthigh and everestlow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

3 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
anc_everest Aggregated anchoring response, combining variables everesthigh and everestlow in one variable numeric 3 573 576 9744.01 13435.48 8 7500 8700 9000 2e+05 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_chicago

Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

2 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
anc_chicago Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable numeric 2 574 576 4.47 6.66 1e-04 2 3 5 80 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender_r

Gender variable cleaned for grammar, language variations and orthography

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

9 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
gender_r Gender variable cleaned for grammar, language variations and orthography character 9 567 576 0 3 4 10
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

oq

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

70 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name data_type missing complete n empty n_unique min max
oq character 70 506 576 0 501 2 897
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

everesthigh

Anchoring paradigm, high anchor: Height of Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

279 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
everesthigh Anchoring paradigm, high anchor: Height of Mount Everest numeric 279 297 576 12263.18 14973.82 8.85 8600 8900 13500 2e+05 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

chicagohigh

Anchoring paradigm, high anchor: Population of Chicago

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

279 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
chicagohigh Anchoring paradigm, high anchor: Population of Chicago numeric 279 297 576 325418.21 1322566.96 1 3 4 7 1e+07 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

babieshigh

Anchoring paradigm, high anchor: Babies born each day

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

280 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
babieshigh Anchoring paradigm, high anchor: Babies born each day numeric 280 296 576 123054.09 490175.91 1.8 10800 30000 60000 4e+06 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

everestlow

Anchoring paradigm, low anchor: Height of Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

300 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
everestlow Anchoring paradigm, low anchor: Height of Mount Everest numeric 300 276 576 7033.17 10949.84 8 2000 8000 8500 120000 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

chicagolow

Anchoring paradigm, low anchor: Population of Chicago

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

299 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
chicagolow Anchoring paradigm, low anchor: Population of Chicago numeric 299 277 576 257166.88 3072380.11 0.1 1 2.5 5 5e+07 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

babieslow

Anchoring paradgim, low anchor: Babies born each day

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

299 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
babieslow Anchoring paradgim, low anchor: Babies born each day integer 299 277 576 61888.21 387993.71 2 300 1000 10000 4e+06 ▇▁▁▁▁▁▁▁
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d1

NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d1 NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained? character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d2.sq001

NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d2.sq001 NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : Yes, I remember character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d2.sq002

NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d2.sq002 NOT USED Answer option to control question d2: ‘Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?’ : No, I don’t remember character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq001

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d3.sq001 NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: Yes
character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq002

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d3.sq002 NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: No character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

d3.sq003

NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
d3.sq003 NOT USED Answer option to control question d2: ‘Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?’: I don’t remember character 0 576 576 0 3 2 3
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender

Gender: open-entry self-report

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

6 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
gender Gender: open-entry self-report character 6 570 576 0 22 1 97
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

age

Age categories

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

6 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
age Age categories character 6 570 576 0 8 13 13
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

end

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

531 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name data_type missing complete n empty n_unique min max
end character 531 45 576 0 44 1 392
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

return

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}
## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}
## No non-missing values to show.
knitr::opts_chunk$set(fig.height = old_height)

576 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name data_type missing complete n count mean
return logical 576 0 576 576 NaN
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

lastpage

Last page completed by the participant, page 12 and 13 are considered as full participation

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
lastpage Last page completed by the participant, page 12 and 13 are considered as full participation integer 0 576 576 12.19 0.39 12 12 12 12 13 ▇▁▁▁▁▁▁▂
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

random

Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n mean sd p0 p25 p50 p75 p100 hist
random Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor. integer 0 576 576 2.57 1.09 1 2 3 3 4 ▆▁▆▁▁▇▁▆
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

cb

No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

313 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
cb No data sharing policy consent presented. One participant clicked on ‘I disagree’ but contacted the first author by email to indicate that they had ‘a bug’ and was unable to complete the questionnaire. See manuscript for details character 313 263 576 0 2 7 10
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

ca

Data sharing policy presented

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

263 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
ca Data sharing policy presented character 263 313 576 0 1 7 7
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_1

comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
mc_1 comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes? character 1 575 576 0 3 2 16
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_2

comprehension question consent 2 (distractor): Is your data anonymous?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
mc_2 comprehension question consent 2 (distractor): Is your data anonymous? character 1 575 576 0 3 2 16
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_3

comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
mc_3 comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared? character 1 575 576 0 3 2 16
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_4

comprehension question consent 3 (distractor): Can you stop your participation at any time?

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))
name label data_type missing complete n empty n_unique min max
mc_4 comprehension question consent 3 (distractor): Can you stop your participation at any time? character 1 575 576 0 3 2 16
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}
if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

Scale: Extraversion

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Cronbach Alpha 0.7264
Spearman Brown 0.7268

Positive correlations: 1 out of 1 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_1, bf_7R
##               Observations: 575
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.73
##           Cronbach's alpha: 0.73
##        Pearson Correlation: 0.57
## 
## 
## Eigen values: 1.571, 0.429NULL
## 
##       vars   n mean   sd median trimmed  mad min max range skew kurtosis
## bf_1     1 575 3.73 1.76      4    3.72 1.48   1   7     6 0.09    -1.12
## bf_7R    2 575 3.33 1.69      3    3.24 1.48   1   7     6 0.49    -0.74
##         se
## bf_1  0.07
## bf_7R 0.07

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
bf_1 TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic. integer 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 3.73 1.76 1 2 4 5 7 ▅▇▇▃▁▇▅▂
bf_7R TIPI item 6, Extraversion: I see myself as reserved, quiet. numeric 7. Disagree strongly,
6. Disagree moderately,
5. Disagree a little,
4. Neither agree nor disagree,
3. Agree a little,
2. Agree moderately,
1. Agree strongly
1 575 576 3.33 1.69 1 2 3 5 7 ▃▇▇▃▁▃▃▁

Scale: Agreeableness

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Cronbach Alpha -0.4274
Spearman Brown -0.4383

Positive correlations: 0 out of 1 (0%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_2R, bf_8
##               Observations: 575
##      Positive correlations: 0 out of 1 (0%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: -0.44
##           Cronbach's alpha: -0.43
##        Pearson Correlation: -0.18
## 
## 
## Eigen values: 1.18, 0.82NULL
## 
##       vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_2R    1 575  4.2 1.57      5    4.27 1.48   1   7     6 -0.38    -0.79
## bf_8     2 575  5.4 1.28      6    5.56 1.48   1   7     6 -1.06     1.14
##         se
## bf_2R 0.07
## bf_8  0.05

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
bf_2R TIPI item 2, Agreeableness: I see myself as critical, quarrelsome. numeric 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 4.2 1.57 1 3 5 5 7 ▂▃▃▃▁▇▃▁
bf_8 TIPI item 7, Agreeableness: I see myself as sympathetic, warm. integer 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 5.4 1.28 1 5 6 6 7 ▁▁▁▂▁▆▇▃

Scale: Conscientiousness

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Cronbach Alpha 0.4871
Spearman Brown 0.4927

Positive correlations: 1 out of 1 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_3, bf_10R
##               Observations: 575
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.49
##           Cronbach's alpha: 0.49
##        Pearson Correlation: 0.33
## 
## 
## Eigen values: 1.327, 0.673NULL
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_3      1 575 5.08 1.36      5    5.19 1.48   1   7     6 -0.79     0.25
## bf_10R    2 575 4.92 1.62      5    5.02 1.48   1   7     6 -0.44    -0.87
##          se
## bf_3   0.06
## bf_10R 0.07

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
bf_3 TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined. integer 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 5.08 1.36 1 5 5 6 7 ▁▁▂▂▁▇▇▃
bf_10R TIPI item 8, Conscientiousness: I see myself as disorganized, careless. numeric 7. Disagree strongly,
6. Disagree moderately,
5. Disagree a little,
4. Neither agree nor disagree,
3. Agree a little,
2. Agree moderately,
1. Agree strongly
1 575 576 4.92 1.62 1 3.5 5 6 7 ▁▂▅▃▁▆▇▆

Scale: Neuroticism

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Cronbach Alpha -2.331
Spearman Brown -2.382

Positive correlations: 0 out of 1 (0%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_5R, bf_11
##               Observations: 575
##      Positive correlations: 0 out of 1 (0%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: -2.38
##           Cronbach's alpha: -2.33
##        Pearson Correlation: -0.54
## 
## 
## Eigen values: 1.544, 0.456NULL
## 
##       vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_5R    1 575 4.09 1.78      4    4.10 1.48   1   7     6 -0.11    -1.13
## bf_11    2 575 4.63 1.55      5    4.71 1.48   1   7     6 -0.42    -0.70
##         se
## bf_5R 0.07
## bf_11 0.06

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
bf_5R TIPI item 4, Neuroticsm: I see myself as anxious, easily upset. numeric 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 4.09 1.78 1 2 4 5 7 ▂▆▅▃▁▇▅▃
bf_11 TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable. integer 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 4.63 1.55 1 3 5 6 7 ▁▂▅▅▁▇▇▂

Scale: Openness to experience

Overview

Reliability: .

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Cronbach Alpha 0.463
Spearman Brown 0.4708

Positive correlations: 1 out of 1 (100%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_6, bf_12R
##               Observations: 575
##      Positive correlations: 1 out of 1 (100%)
## 
## Estimates assuming interval level:
## 
## Spearman Brown coefficient: 0.47
##           Cronbach's alpha: 0.46
##        Pearson Correlation: 0.31
## 
## 
## Eigen values: 1.308, 0.692NULL
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_6      1 575 5.24 1.27      5    5.35 1.48   1   7     6 -0.75     0.45
## bf_12R    2 575 4.83 1.57      5    4.93 1.48   1   7     6 -0.49    -0.62
##          se
## bf_6   0.05
## bf_12R 0.07

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
bf_6 TIPI item 5, Openness to experience: I see myself as open to new experiences, complex. integer 1. Disagree strongly,
2. Disagree moderately,
3. Disagree a little,
4. Neither agree nor disagree,
5. Agree a little,
6. Agree moderately,
7. Agree strongly
1 575 576 5.24 1.27 1 5 5 6 7 ▁▁▂▂▁▇▇▃
bf_12R TIPI item 10, Openness to experience: I see myself as conventional, uncreative. numeric 7. Disagree strongly,
6. Disagree moderately,
5. Disagree a little,
4. Neither agree nor disagree,
3. Agree a little,
2. Agree moderately,
1. Agree strongly
1 575 576 4.83 1.57 1 4 5 6 7 ▁▂▃▅▁▆▇▃

Scale: Social Desirability

Overview

Reliability: ωordinal [95% CI] = 0.18 [0.13;0.24].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.08999
Omega Psych Tot 0.7091
Omega Psych H 0.3512
Omega Ordinal 0.1847
Cronbach Alpha 0.4667
Greatest Lower Bound 0.6804
Alpha Ordinal 0.6117

Positive correlations: 308 out of 528 (58%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33
##               Observations: 575
##      Positive correlations: 308 out of 528 (58%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.09
##       Omega (hierarchical): 0.35
##    Revelle's omega (total): 0.71
## Greatest Lower Bound (GLB): 0.68
##              Coefficient H: 0.74
##           Cronbach's alpha: 0.47
## Confidence intervals:
##              Omega (total): [0.03, 0.15]
##           Cronbach's alpha: [0.4, 0.53]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.18
##  Ordinal Omega (hierarch.): 0.08
##   Ordinal Cronbach's alpha: 0.61
## Confidence intervals:
##      Ordinal Omega (total): [0.13, 0.24]
##   Ordinal Cronbach's alpha: [0.57, 0.66]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.385, 2.03, 1.688, 1.428, 1.328, 1.21, 1.153, 1.124, 1.081, 1.055, 1.041, 1, 0.977, 0.956, 0.908, 0.878, 0.871, 0.867, 0.827, 0.81, 0.798, 0.755, 0.72, 0.713, 0.687, 0.686, 0.658, 0.623, 0.609, 0.571, 0.549, 0.522, 0.492
## Loadings:
##           TC10   TC4    TC12   TC9    TC2    TC1    TC7    TC5    TC6   
## soc_d_1          -0.190  0.283                              0.224       
## soc_d_2           0.105                       0.727                     
## soc_d_3R   0.747                                                  -0.143
## soc_d_4          -0.476         0.367        -0.205 -0.120         0.238
## soc_d_5R   0.395                                    -0.114 -0.243  0.238
## soc_d_6R   0.106  0.247  0.158        -0.163 -0.387  0.190 -0.127  0.333
## soc_d_7           0.329  0.107                0.300 -0.117  0.229  0.348
## soc_d_8   -0.297  0.195                              0.242  0.184  0.334
## soc_d_9R   0.150         0.228                       0.174  0.139  0.118
## soc_d_10R  0.639                                    -0.129 -0.172       
## soc_d_11R         0.604                                                 
## soc_d_12R         0.213  0.229                              0.255 -0.173
## soc_d_13         -0.105                                            0.750
## soc_d_14R                                            0.103              
## soc_d_15R         0.129  0.312               -0.406  0.225  0.264       
## soc_d_16                -0.189         0.480  0.203                     
## soc_d_17  -0.135         0.146         0.675                            
## soc_d_18                                            -0.121  0.746       
## soc_d_19R                0.751                                          
## soc_d_20                               0.737               -0.108       
## soc_d_21                -0.314         0.118        -0.232  0.265  0.370
## soc_d_22R         0.117  0.220               -0.190  0.555  0.124 -0.132
## soc_d_23R         0.214  0.400        -0.116  0.312        -0.213       
## soc_d_24   0.153  0.155 -0.268         0.156         0.284 -0.118       
## soc_d_25  -0.106        -0.114                0.101  0.721 -0.180       
## soc_d_26   0.288 -0.256 -0.189                0.244  0.307  0.353  0.119
## soc_d_27                 0.147                                          
## soc_d_28R  0.324  0.379  0.181  0.120                      -0.196  0.166
## soc_d_29   0.199 -0.328  0.184  0.569                                   
## soc_d_30R  0.293  0.428        -0.132        -0.252         0.152       
## soc_d_31          0.120         0.612  0.131                            
## soc_d_32R                0.271               -0.255         0.167       
## soc_d_33                        0.717                                   
##           TC3    TC8    TC11  
## soc_d_1           0.118  0.621
## soc_d_2                       
## soc_d_3R                      
## soc_d_4                 -0.105
## soc_d_5R   0.192 -0.122  0.307
## soc_d_6R                 0.147
## soc_d_7   -0.338              
## soc_d_8   -0.155  0.281 -0.104
## soc_d_9R         -0.441 -0.413
## soc_d_10R  0.147         0.134
## soc_d_11R  0.202              
## soc_d_12R  0.358 -0.306  0.221
## soc_d_13                      
## soc_d_14R  0.701              
## soc_d_15R                     
## soc_d_16  -0.146 -0.108  0.122
## soc_d_17                      
## soc_d_18                      
## soc_d_19R                     
## soc_d_20          0.104       
## soc_d_21   0.268         0.202
## soc_d_22R                     
## soc_d_23R  0.338              
## soc_d_24  -0.265 -0.121  0.436
## soc_d_25   0.125              
## soc_d_26   0.174  0.217 -0.138
## soc_d_27          0.789       
## soc_d_28R               -0.169
## soc_d_29  -0.106 -0.102       
## soc_d_30R         0.222 -0.176
## soc_d_31  -0.235        -0.131
## soc_d_32R               -0.242
## soc_d_33   0.144  0.189       
## 
##                 TC10   TC4  TC12   TC9   TC2   TC1   TC7   TC5   TC6   TC3
## SS loadings    1.677 1.570 1.563 1.453 1.416 1.392 1.390 1.382 1.335 1.325
## Proportion Var 0.051 0.048 0.047 0.044 0.043 0.042 0.042 0.042 0.040 0.040
## Cumulative Var 0.051 0.098 0.146 0.190 0.233 0.275 0.317 0.359 0.399 0.439
##                  TC8  TC11
## SS loadings    1.251 1.214
## Proportion Var 0.038 0.037
## Cumulative Var 0.477 0.514
## 
##           vars   n mean   sd median trimmed mad min max range  skew
## soc_d_1      1 575 0.65 0.48      1    0.69   0   0   1     1 -0.63
## soc_d_2      2 575 0.67 0.47      1    0.71   0   0   1     1 -0.70
## soc_d_3R     3 575 0.69 0.46      1    0.74   0   0   1     1 -0.81
## soc_d_4      4 575 0.24 0.43      0    0.17   0   0   1     1  1.24
## soc_d_5R     5 575 0.85 0.36      1    0.93   0   0   1     1 -1.92
## soc_d_6R     6 575 0.71 0.45      1    0.76   0   0   1     1 -0.93
## soc_d_7      7 575 0.52 0.50      1    0.52   0   0   1     1 -0.08
## soc_d_8      8 575 0.52 0.50      1    0.53   0   0   1     1 -0.09
## soc_d_9R     9 575 0.52 0.50      1    0.53   0   0   1     1 -0.09
## soc_d_10R   10 575 0.74 0.44      1    0.80   0   0   1     1 -1.09
## soc_d_11R   11 575 0.69 0.46      1    0.74   0   0   1     1 -0.84
## soc_d_12R   12 575 0.49 0.50      0    0.49   0   0   1     1  0.03
## soc_d_13    13 575 0.72 0.45      1    0.78   0   0   1     1 -1.00
## soc_d_14R   14 575 0.71 0.46      1    0.76   0   0   1     1 -0.90
## soc_d_15R   15 575 0.57 0.50      1    0.59   0   0   1     1 -0.28
## soc_d_16    16 575 0.67 0.47      1    0.72   0   0   1     1 -0.74
## soc_d_17    17 575 0.87 0.33      1    0.97   0   0   1     1 -2.26
## soc_d_18    18 575 0.28 0.45      0    0.23   0   0   1     1  0.96
## soc_d_19R   19 575 0.54 0.50      1    0.54   0   0   1     1 -0.14
## soc_d_20    20 575 0.79 0.40      1    0.87   0   0   1     1 -1.46
## soc_d_21    21 575 0.65 0.48      1    0.68   0   0   1     1 -0.61
## soc_d_22R   22 575 0.79 0.41      1    0.86   0   0   1     1 -1.40
## soc_d_23R   23 575 0.84 0.37      1    0.92   0   0   1     1 -1.82
## soc_d_24    24 575 0.80 0.40      1    0.88   0   0   1     1 -1.51
## soc_d_25    25 575 0.70 0.46      1    0.75   0   0   1     1 -0.87
## soc_d_26    26 575 0.39 0.49      0    0.36   0   0   1     1  0.47
## soc_d_27    27 575 0.52 0.50      1    0.52   0   0   1     1 -0.07
## soc_d_28R   28 575 0.83 0.38      1    0.91   0   0   1     1 -1.72
## soc_d_29    29 575 0.26 0.44      0    0.20   0   0   1     1  1.08
## soc_d_30R   30 575 0.57 0.50      1    0.58   0   0   1     1 -0.26
## soc_d_31    31 575 0.27 0.44      0    0.21   0   0   1     1  1.06
## soc_d_32R   32 575 0.44 0.50      0    0.43   0   0   1     1  0.23
## soc_d_33    33 575 0.31 0.46      0    0.26   0   0   1     1  0.82
##           kurtosis   se
## soc_d_1      -1.61 0.02
## soc_d_2      -1.51 0.02
## soc_d_3R     -1.34 0.02
## soc_d_4      -0.47 0.02
## soc_d_5R      1.70 0.02
## soc_d_6R     -1.14 0.02
## soc_d_7      -2.00 0.02
## soc_d_8      -1.99 0.02
## soc_d_9R     -2.00 0.02
## soc_d_10R    -0.82 0.02
## soc_d_11R    -1.30 0.02
## soc_d_12R    -2.00 0.02
## soc_d_13     -1.01 0.02
## soc_d_14R    -1.19 0.02
## soc_d_15R    -1.92 0.02
## soc_d_16     -1.45 0.02
## soc_d_17      3.11 0.01
## soc_d_18     -1.08 0.02
## soc_d_19R    -1.98 0.02
## soc_d_20      0.12 0.02
## soc_d_21     -1.63 0.02
## soc_d_22R    -0.03 0.02
## soc_d_23R     1.30 0.02
## soc_d_24      0.28 0.02
## soc_d_25     -1.25 0.02
## soc_d_26     -1.79 0.02
## soc_d_27     -2.00 0.02
## soc_d_28R     0.95 0.02
## soc_d_29     -0.84 0.02
## soc_d_30R    -1.93 0.02
## soc_d_31     -0.89 0.02
## soc_d_32R    -1.95 0.02
## soc_d_33     -1.33 0.02

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
soc_d_1 Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates. integer 0. No,
1. Yes
1 575 576 0.65 0.48 0 0 1 1 1 ▅▁▁▁▁▁▁▇
soc_d_2 Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble. integer 0. No,
1. Yes
1 575 576 0.67 0.47 0 0 1 1 1 ▅▁▁▁▁▁▁▇
soc_d_3R Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged. numeric 0. No,
1. Yes
1 575 576 0.69 0.46 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_4 Social desirability questionnaire item 4: I have never intensely disliked anyone. integer 0. No,
1. Yes
1 575 576 0.24 0.43 0 0 0 0 1 ▇▁▁▁▁▁▁▂
soc_d_5R Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life. numeric 0. No,
1. Yes
1 575 576 0.85 0.36 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_6R Social desirability questionnaire item 6: I sometimes feel resentful when I don’t get my way. numeric 0. No,
1. Yes
1 575 576 0.71 0.45 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_7 Social desirability questionnaire item 7: I am always careful about my manner of dress. integer 0. No,
1. Yes
1 575 576 0.52 0.5 0 0 1 1 1 ▇▁▁▁▁▁▁▇
soc_d_8 Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant. integer 0. No,
1. Yes
1 575 576 0.52 0.5 0 0 1 1 1 ▇▁▁▁▁▁▁▇
soc_d_9R Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it. numeric 0. No,
1. Yes
1 575 576 0.52 0.5 0 0 1 1 1 ▇▁▁▁▁▁▁▇
soc_d_10R Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability. numeric 0. No,
1. Yes
1 575 576 0.74 0.44 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_11R Social desirability questionnaire item 11: I like to gossip at times. numeric 0. No,
1. Yes
1 575 576 0.69 0.46 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_12R Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right. numeric 0. No,
1. Yes
1 575 576 0.49 0.5 0 0 0 1 1 ▇▁▁▁▁▁▁▇
soc_d_13 Social desirability questionnaire item 13: No matter who I’m talking to, I’m always a good listener. integer 0. No,
1. Yes
1 575 576 0.72 0.45 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_14R Social desirability questionnaire item 14: I can remember ‘playing sick’ to get out of something. numeric 0. No,
1. Yes
1 575 576 0.71 0.46 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_15R Social desirability questionnaire item 15: There have been occasions when I took advantage of someone. numeric 0. No,
1. Yes
1 575 576 0.57 0.5 0 0 1 1 1 ▆▁▁▁▁▁▁▇
soc_d_16 Social desirability questionnaire item 16: I’m always willing to admit it when I make a mistake. integer 0. No,
1. Yes
1 575 576 0.67 0.47 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_17 Social desirability questionnaire item 17: I always try to practice what I preach. integer 0. No,
1. Yes
1 575 576 0.87 0.33 0 1 1 1 1 ▁▁▁▁▁▁▁▇
soc_d_18 Social desirability questionnaire item 18: I don’t find it particularly difficult to get along with loud mouthed, obnoxious people. integer 0. No,
1. Yes
1 575 576 0.28 0.45 0 0 0 1 1 ▇▁▁▁▁▁▁▃
soc_d_19R Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget. numeric 0. No,
1. Yes
1 575 576 0.54 0.5 0 0 1 1 1 ▇▁▁▁▁▁▁▇
soc_d_20 Social desirability questionnaire item 20: When I don’t know something I don’t at all mind admitting it. integer 0. No,
1. Yes
1 575 576 0.79 0.4 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_21 Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable. integer 0. No,
1. Yes
1 575 576 0.65 0.48 0 0 1 1 1 ▅▁▁▁▁▁▁▇
soc_d_22R Social desirability questionnaire item 22: At times I have really insisted on having things my own way. numeric 0. No,
1. Yes
1 575 576 0.79 0.41 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_23R Social desirability questionnaire item 23: There have been occasions when I felt like smashing things. numeric 0. No,
1. Yes
1 575 576 0.84 0.37 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_24 Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings. integer 0. No,
1. Yes
1 575 576 0.8 0.4 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_25 Social desirability questionnaire item 25: I never resent being asked to return a favor. integer 0. No,
1. Yes
1 575 576 0.7 0.46 0 0 1 1 1 ▃▁▁▁▁▁▁▇
soc_d_26 Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own. integer 0. No,
1. Yes
1 575 576 0.39 0.49 0 0 0 1 1 ▇▁▁▁▁▁▁▅
soc_d_27 Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car. integer 0. No,
1. Yes
1 575 576 0.52 0.5 0 0 1 1 1 ▇▁▁▁▁▁▁▇
soc_d_28R Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others. numeric 0. No,
1. Yes
1 575 576 0.83 0.38 0 1 1 1 1 ▂▁▁▁▁▁▁▇
soc_d_29 Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off. integer 0. No,
1. Yes
1 575 576 0.26 0.44 0 0 0 1 1 ▇▁▁▁▁▁▁▃
soc_d_30R Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. numeric 0. No,
1. Yes
1 575 576 0.57 0.5 0 0 1 1 1 ▆▁▁▁▁▁▁▇
soc_d_31 Social desirability questionnaire item 31: I have never felt that I was punished without cause. integer 0. No,
1. Yes
1 575 576 0.27 0.44 0 0 0 1 1 ▇▁▁▁▁▁▁▃
soc_d_32R Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved. numeric 0. No,
1. Yes
1 575 576 0.44 0.5 0 0 0 1 1 ▇▁▁▁▁▁▁▆
soc_d_33 Social desirability questionnaire item 33: I have never deliberately said something that hurt someone’s feelings. integer 0. No,
1. Yes
1 575 576 0.31 0.46 0 0 0 1 1 ▇▁▁▁▁▁▁▃

Scale: Careless responses

Overview

Reliability: ωordinal [95% CI] = 0.5 [0.48;0.53].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)
if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}
Reliability Indices
coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)
Index Estimate
Omega 0.271
Omega Psych Tot 0.6747
Omega Psych H 0.3694
Omega Ordinal 0.5038
Cronbach Alpha 0.1744
Greatest Lower Bound 0.5315
Alpha Ordinal 0.6065

Positive correlations: 13 out of 21 (62%)

Scatter matrix
print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)
## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7
##               Observations: 575
##      Positive correlations: 13 out of 21 (62%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.27
##       Omega (hierarchical): 0.37
##    Revelle's omega (total): 0.67
## Greatest Lower Bound (GLB): 0.53
##              Coefficient H: 0.61
##           Cronbach's alpha: 0.17
## Confidence intervals:
##              Omega (total): [0.2, 0.35]
##           Cronbach's alpha: [0.09, 0.26]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.5
##  Ordinal Omega (hierarch.): 0.3
##   Ordinal Cronbach's alpha: 0.61
## Confidence intervals:
##      Ordinal Omega (total): [0.48, 0.53]
##   Ordinal Cronbach's alpha: [0.56, 0.65]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 1.948, 1.256, 0.961, 0.797, 0.78, 0.704, 0.553
## Loadings:
##       TC1    TC2   
## cr_1R -0.547       
## cr_2   0.652       
## cr_3  -0.151  0.791
## cr_4   0.556  0.122
## cr_5   0.506  0.359
## cr_6   0.177  0.779
## cr_7   0.582 -0.322
## 
##                  TC1   TC2
## SS loadings    1.683 1.490
## Proportion Var 0.240 0.213
## Cumulative Var 0.240 0.453
## 
##       vars   n mean   sd median trimmed mad min max range  skew kurtosis
## cr_1R    1 575 0.91 0.29      1    1.00   0   0   1     1 -2.85     6.13
## cr_2     2 575 0.14 0.35      0    0.05   0   0   1     1  2.08     2.33
## cr_3     3 575 0.03 0.16      0    0.00   0   0   1     1  5.73    30.85
## cr_4     4 575 0.08 0.27      0    0.00   0   0   1     1  3.05     7.29
## cr_5     5 575 0.02 0.14      0    0.00   0   0   1     1  6.69    42.78
## cr_6     6 575 0.01 0.11      0    0.00   0   0   1     1  8.87    76.88
## cr_7     7 575 0.05 0.22      0    0.00   0   0   1     1  4.18    15.52
##         se
## cr_1R 0.01
## cr_2  0.01
## cr_3  0.01
## cr_4  0.01
## cr_5  0.01
## cr_6  0.00
## cr_7  0.01

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))
name label data_type value_labels missing complete n mean sd p0 p25 p50 p75 p100 hist
cr_1R Careless response item 1: I am using an electronic device at this moment. numeric 1. Yes,
0. No
1 575 576 0.91 0.29 0 1 1 1 1 ▁▁▁▁▁▁▁▇
cr_2 Careless response item 2: I turn into a leprechaun at night. integer 0. Yes,
1. No
1 575 576 0.14 0.35 0 0 0 0 1 ▇▁▁▁▁▁▁▁
cr_3 Careless response item 3: All my friends are aliens. integer 0. Yes,
1. No
1 575 576 0.028 0.16 0 0 0 0 1 ▇▁▁▁▁▁▁▁
cr_4 Careless response item 4: All my friends say I would make a great poodle. integer 0. Yes,
1. No
1 575 576 0.082 0.27 0 0 0 0 1 ▇▁▁▁▁▁▁▁
cr_5 Careless response item 5: I eat cement occasionally. integer 0. Yes,
1. No
1 575 576 0.021 0.14 0 0 0 0 1 ▇▁▁▁▁▁▁▁
cr_6 Careless response item 6: I can teleport across time and space. integer 0. Yes,
1. No
1 575 576 0.012 0.11 0 0 0 0 1 ▇▁▁▁▁▁▁▁
cr_7 Careless response item 7: I will be punished for meeting the requirements of my job. integer 0. Yes,
1. No
1 575 576 0.049 0.22 0 0 0 0 1 ▇▁▁▁▁▁▁▁
missingness_report

Missingness report

if (length(md_pattern)) {
  if (knitr::is_html_output()) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}
items

Codebook table

export_table(metadata_table)
jsonld

JSON-LD metadata The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Online (Prolific.co) data on TIPI, Social Desirability, Careless Response and Anchoring Paradigm, public data set",
  "description": "10 items taking from the Very brief measure of the Big 5 Personality questionnaire (Gosling, Rentfrow, & Swann, 2003), 33 items from the Social desirability scale (Crowne & Marlowe, 1960) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This dataset cannot be publicly shared in its entirety, as the study consent stated to half the participants that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors. Please find the shareable half of the data set on our osf.io page (see doi)\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name                   |label                                                                                                                                                                                                                                            |data_type |value_labels                                                                                                                                                 |scale_item_names                                                                                                                                                                                                                                                                                                                               |missing |complete |n   |empty |n_unique |count |min |max |mean      |sd         |p0    |p25    |p50   |p75    |p100   |hist     |\n|:----------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------|:--------|:---|:-----|:--------|:-----|:---|:---|:---------|:----------|:-----|:------|:-----|:------|:------|:--------|\n|V1                     |NA                                                                                                                                                                                                                                               |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |NA    |NA       |NA    |NA  |NA  |288.5     |166.42     |1     |144.75 |288.5 |432.25 |576    |▇▇▇▇▇▇▇▇ |\n|id                     |ID variable from raw data                                                                                                                                                                                                                        |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |NA    |NA       |NA    |NA  |NA  |318.87    |187.35     |1     |156.75 |317.5 |480.25 |654    |▇▇▇▇▇▇▇▆ |\n|consent                |Variables cb and ca combined in one variable                                                                                                                                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |2        |NA    |1   |1   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|cond_anc               |Anchoring condition: high and low                                                                                                                                                                                                                |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |2        |NA    |1   |1   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|refused                |Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition. |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |NA    |NA       |NA    |NA  |NA  |0.0017    |0.042      |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|remember               |Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?                                                                                                                                           |integer   |0. No or wrong memory, - 1. correct memory                                                                                                                     |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.55      |0.5        |0     |0      |1     |1      |1      |▆▁▁▁▁▁▁▇ |\n|anc_baby               |Aggregated anchoring response, combining variables babieshigh and babieslow in one variable                                                                                                                                                      |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |3       |573      |576 |NA    |NA       |NA    |NA  |NA  |93485.25  |444398.92  |1.8   |1000   |10000 |40000  |4e+06  |▇▁▁▁▁▁▁▁ |\n|anc_everest            |Aggregated anchoring response, combining variables everesthigh and everestlow in one variable                                                                                                                                                    |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |3       |573      |576 |NA    |NA       |NA    |NA  |NA  |9744.01   |13435.48   |8     |7500   |8700  |9000   |2e+05  |▇▁▁▁▁▁▁▁ |\n|anc_chicago            |Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable                                                                                                                                                    |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |2       |574      |576 |NA    |NA       |NA    |NA  |NA  |4.47      |6.66       |1e-04 |2      |3     |5      |80     |▇▁▁▁▁▁▁▁ |\n|gender_r               |Gender variable cleaned for grammar, language variations and orthography                                                                                                                                                                         |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |9       |567      |576 |0     |3        |NA    |4   |10  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|oq                     |NA                                                                                                                                                                                                                                               |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |70      |506      |576 |0     |501      |NA    |2   |897 |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|bf_1                   |TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.                                                                                                                                                                           |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |3.73      |1.76       |1     |2      |4     |5      |7      |▅▇▇▃▁▇▅▂ |\n|bf_2R                  |TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.                                                                                                                                                                               |numeric   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.2       |1.57       |1     |3      |5     |5      |7      |▂▃▃▃▁▇▃▁ |\n|bf_3                   |TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.                                                                                                                                                                    |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |5.08      |1.36       |1     |5      |5     |6      |7      |▁▁▂▂▁▇▇▃ |\n|bf_5R                  |TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.                                                                                                                                                                                  |numeric   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.09      |1.78       |1     |2      |4     |5      |7      |▂▆▅▃▁▇▅▃ |\n|bf_6                   |TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.                                                                                                                                                           |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |5.24      |1.27       |1     |5      |5     |6      |7      |▁▁▂▂▁▇▇▃ |\n|bf_7R                  |TIPI item 6, Extraversion: I see myself as reserved, quiet.                                                                                                                                                                                      |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |3.33      |1.69       |1     |2      |3     |5      |7      |▃▇▇▃▁▃▃▁ |\n|bf_8                   |TIPI item 7, Agreeableness: I see myself as sympathetic, warm.                                                                                                                                                                                   |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |5.4       |1.28       |1     |5      |6     |6      |7      |▁▁▁▂▁▆▇▃ |\n|bf_10R                 |TIPI item 8, Conscientiousness: I see myself as disorganized, careless.                                                                                                                                                                          |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.92      |1.62       |1     |3.5    |5     |6      |7      |▁▂▅▃▁▆▇▆ |\n|bf_11                  |TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.                                                                                                                                                                               |integer   |1. Disagree strongly, - 2. Disagree moderately, - 3. Disagree a little, - 4. Neither agree nor disagree, - 5. Agree a little, - 6. Agree moderately, - 7. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.63      |1.55       |1     |3      |5     |6      |7      |▁▂▅▅▁▇▇▂ |\n|bf_12R                 |TIPI item 10, Openness to experience: I see myself as conventional, uncreative.                                                                                                                                                                  |numeric   |7. Disagree strongly, - 6. Disagree moderately, - 5. Disagree a little, - 4. Neither agree nor disagree, - 3. Agree a little, - 2. Agree moderately, - 1. Agree strongly |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.83      |1.57       |1     |4      |5     |6      |7      |▁▂▃▅▁▆▇▃ |\n|soc_d_1                |Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.65      |0.48       |0     |0      |1     |1      |1      |▅▁▁▁▁▁▁▇ |\n|soc_d_2                |Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.                                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.67      |0.47       |0     |0      |1     |1      |1      |▅▁▁▁▁▁▁▇ |\n|soc_d_3R               |Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.                                                                                                                              |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.69      |0.46       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_4                |Social desirability questionnaire item 4: I have never intensely disliked anyone.                                                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.24      |0.43       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▂ |\n|soc_d_5R               |Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.                                                                                                                                     |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.85      |0.36       |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_6R               |Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.                                                                                                                                                    |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.71      |0.45       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_7                |Social desirability questionnaire item 7: I am always careful about my manner of dress.                                                                                                                                                          |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.52      |0.5        |0     |0      |1     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_8                |Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.52      |0.5        |0     |0      |1     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_9R               |Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.                                                                                                          |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.52      |0.5        |0     |0      |1     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_10R              |Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.                                                                                                       |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.74      |0.44       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_11R              |Social desirability questionnaire item 11: I like to gossip at times.                                                                                                                                                                            |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.69      |0.46       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_12R              |Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.                                                                                      |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.49      |0.5        |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_13               |Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.                                                                                                                                             |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.72      |0.45       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_14R              |Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.                                                                                                                                                |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.71      |0.46       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_15R              |Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.57      |0.5        |0     |0      |1     |1      |1      |▆▁▁▁▁▁▁▇ |\n|soc_d_16               |Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.                                                                                                                                                 |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.67      |0.47       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_17               |Social desirability questionnaire item 17: I always try to practice what I preach.                                                                                                                                                               |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.87      |0.33       |0     |1      |1     |1      |1      |▁▁▁▁▁▁▁▇ |\n|soc_d_18               |Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.                                                                                                              |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.28      |0.45       |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▃ |\n|soc_d_19R              |Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.54      |0.5        |0     |0      |1     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_20               |Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.                                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.79      |0.4        |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_21               |Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.                                                                                                                                           |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.65      |0.48       |0     |0      |1     |1      |1      |▅▁▁▁▁▁▁▇ |\n|soc_d_22R              |Social desirability questionnaire item 22: At times I have really insisted on having things my own way.                                                                                                                                          |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.79      |0.41       |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_23R              |Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.                                                                                                                                           |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.84      |0.37       |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_24               |Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.8       |0.4        |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_25               |Social desirability questionnaire item 25: I never resent being asked to return a favor.                                                                                                                                                         |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.7       |0.46       |0     |0      |1     |1      |1      |▃▁▁▁▁▁▁▇ |\n|soc_d_26               |Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.39      |0.49       |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▅ |\n|soc_d_27               |Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.                                                                                                                                       |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.52      |0.5        |0     |0      |1     |1      |1      |▇▁▁▁▁▁▁▇ |\n|soc_d_28R              |Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.                                                                                                                         |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.83      |0.38       |0     |1      |1     |1      |1      |▂▁▁▁▁▁▁▇ |\n|soc_d_29               |Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.                                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.26      |0.44       |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▃ |\n|soc_d_30R              |Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me.                                                                                                                                              |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.57      |0.5        |0     |0      |1     |1      |1      |▆▁▁▁▁▁▁▇ |\n|soc_d_31               |Social desirability questionnaire item 31: I have never felt that I was punished without cause.                                                                                                                                                  |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.27      |0.44       |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▃ |\n|soc_d_32R              |Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.                                                                                                                     |numeric   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.44      |0.5        |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▆ |\n|soc_d_33               |Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.                                                                                                                                |integer   |0. No, - 1. Yes                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.31      |0.46       |0     |0      |0     |1      |1      |▇▁▁▁▁▁▁▃ |\n|cr_1R                  |Careless response item 1: I am using an electronic device at this moment.                                                                                                                                                                        |numeric   |1. Yes, - 0. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.91      |0.29       |0     |1      |1     |1      |1      |▁▁▁▁▁▁▁▇ |\n|cr_2                   |Careless response item 2: I turn into a leprechaun at night.                                                                                                                                                                                     |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.14      |0.35       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|cr_3                   |Careless response item 3: All my friends are aliens.                                                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.028     |0.16       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|cr_4                   |Careless response item 4: All my friends say I would make a great poodle.                                                                                                                                                                        |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.082     |0.27       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|cr_5                   |Careless response item 5: I eat cement occasionally.                                                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.021     |0.14       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|cr_6                   |Careless response item 6: I can teleport across time and space.                                                                                                                                                                                  |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.012     |0.11       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|cr_7                   |Careless response item 7: I will be punished for meeting the requirements of my job.                                                                                                                                                             |integer   |0. Yes, - 1. No                                                                                                                                                |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.049     |0.22       |0     |0      |0     |0      |1      |▇▁▁▁▁▁▁▁ |\n|everesthigh            |Anchoring paradigm, high anchor: Height of Mount Everest                                                                                                                                                                                         |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |279     |297      |576 |NA    |NA       |NA    |NA  |NA  |12263.18  |14973.82   |8.85  |8600   |8900  |13500  |2e+05  |▇▁▁▁▁▁▁▁ |\n|chicagohigh            |Anchoring paradigm, high anchor: Population of Chicago                                                                                                                                                                                           |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |279     |297      |576 |NA    |NA       |NA    |NA  |NA  |325418.21 |1322566.96 |1     |3      |4     |7      |1e+07  |▇▁▁▁▁▁▁▁ |\n|babieshigh             |Anchoring paradigm, high anchor: Babies born each day                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |280     |296      |576 |NA    |NA       |NA    |NA  |NA  |123054.09 |490175.91  |1.8   |10800  |30000 |60000  |4e+06  |▇▁▁▁▁▁▁▁ |\n|everestlow             |Anchoring paradigm, low anchor: Height of Mount Everest                                                                                                                                                                                          |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |300     |276      |576 |NA    |NA       |NA    |NA  |NA  |7033.17   |10949.84   |8     |2000   |8000  |8500   |120000 |▇▁▁▁▁▁▁▁ |\n|chicagolow             |Anchoring paradigm, low anchor: Population of Chicago                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |299     |277      |576 |NA    |NA       |NA    |NA  |NA  |257166.88 |3072380.11 |0.1   |1      |2.5   |5      |5e+07  |▇▁▁▁▁▁▁▁ |\n|babieslow              |Anchoring paradgim, low anchor: Babies born each day                                                                                                                                                                                             |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |299     |277      |576 |NA    |NA       |NA    |NA  |NA  |61888.21  |387993.71  |2     |300    |1000  |10000  |4e+06  |▇▁▁▁▁▁▁▁ |\n|d1                     |NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?                                                            |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|d2.sq001               |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|d2.sq002               |NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember                                                                |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|d3.sq001               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|d3.sq002               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No                                                                                      |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|d3.sq003               |NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember                                                                        |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |0     |3        |NA    |2   |3   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|gender                 |Gender: open-entry self-report                                                                                                                                                                                                                   |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |6       |570      |576 |0     |22       |NA    |1   |97  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|age                    |Age categories                                                                                                                                                                                                                                   |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |6       |570      |576 |0     |8        |NA    |13  |13  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|end                    |NA                                                                                                                                                                                                                                               |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |531     |45       |576 |0     |44       |NA    |1   |392 |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|return                 |NA                                                                                                                                                                                                                                               |logical   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |576     |0        |576 |NA    |NA       |576   |NA  |NA  |NaN       |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|lastpage               |Last page completed by the participant, page 12 and 13 are considered as full participation                                                                                                                                                      |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |NA    |NA       |NA    |NA  |NA  |12.19     |0.39       |12    |12     |12    |12     |13     |▇▁▁▁▁▁▁▂ |\n|random                 |Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.                                                                                                          |integer   |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |0       |576      |576 |NA    |NA       |NA    |NA  |NA  |2.57      |1.09       |1     |2      |3     |3      |4      |▆▁▆▁▁▇▁▆ |\n|cb                     |No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details             |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |313     |263      |576 |0     |2        |NA    |7   |10  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|ca                     |Data sharing policy presented                                                                                                                                                                                                                    |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |263     |313      |576 |0     |1        |NA    |7   |7   |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|mc_1                   |comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?                                                                                                                                                     |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|mc_2                   |comprehension question consent 2 (distractor): Is your data anonymous?                                                                                                                                                                           |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|mc_3                   |comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?                                                                                                              |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|mc_4                   |comprehension question consent 3 (distractor): Can you stop your participation at any time?                                                                                                                                                      |character |NA                                                                                                                                                           |NA                                                                                                                                                                                                                                                                                                                                             |1       |575      |576 |0     |3        |NA    |2   |16  |NA        |NA         |NA    |NA     |NA    |NA     |NA     |NA       |\n|Extraversion           |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_1, bf_7R                                                                                                                                                                                                                                                                                                                                    |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |3.53      |1.53       |1     |2.5    |3.5   |4.5    |7      |▅▇▃▇▂▅▂▂ |\n|Agreeableness          |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_2R, bf_8                                                                                                                                                                                                                                                                                                                                    |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.6       |1.1        |1     |4      |4.5   |5.5    |7      |▁▁▁▇▆▇▂▂ |\n|Conscientiousness      |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_3, bf_10R                                                                                                                                                                                                                                                                                                                                   |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |5         |1.22       |1.5   |4      |5     |6      |7      |▁▁▃▃▃▇▃▅ |\n|Neuroticism            |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_5R, bf_11                                                                                                                                                                                                                                                                                                                                   |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |4.27      |1.47       |1     |3      |4.5   |5.5    |7      |▁▅▃▇▃▇▃▃ |\n|Openness to experience |2 bf items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |bf_6, bf_12R                                                                                                                                                                                                                                                                                                                                   |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |5.04      |1.15       |1.5   |4.25   |5     |6      |7      |▁▁▃▃▅▇▅▃ |\n|Social Desirability    |33 soc_d items aggregated by rowMeans                                                                                                                                                                                                            |numeric   |NA                                                                                                                                                           |soc_d_1, soc_d_2, soc_d_3R, soc_d_4, soc_d_5R, soc_d_6R, soc_d_7, soc_d_8, soc_d_9R, soc_d_10R, soc_d_11R, soc_d_12R, soc_d_13, soc_d_14R, soc_d_15R, soc_d_16, soc_d_17, soc_d_18, soc_d_19R, soc_d_20, soc_d_21, soc_d_22R, soc_d_23R, soc_d_24, soc_d_25, soc_d_26, soc_d_27, soc_d_28R, soc_d_29, soc_d_30R, soc_d_31, soc_d_32R, soc_d_33 |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.45      |0.14       |0.03  |0.36   |0.45  |0.55   |0.88   |▁▂▅▇▆▅▁▁ |\n|Careless responses     |7 cr items aggregated by rowMeans                                                                                                                                                                                                                |numeric   |NA                                                                                                                                                           |cr_1R, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7                                                                                                                                                                                                                                                                                                      |1       |575      |576 |NA    |NA       |NA    |NA  |NA  |0.18      |0.096      |0     |0.14   |0.14  |0.14   |0.71   |▁▇▁▂▁▁▁▁ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
  "identifier": "https://doi.org/10.17605/OSF.IO/AM6BC",
  "creator": "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein",
  "citation": "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Data collected online. https://doi.org/10.17605/OSF.IO/AM6BC",
  "datePublished": "2019-08-06",
  "temporalCoverage": "2019-06-17 to 2019-06-21",
  "spatialCoverage": "Online participants residing in, or citizens of, the EU at time of data collection",
  "keywords": ["V1", "id", "consent", "cond_anc", "refused", "remember", "anc_baby", "anc_everest", "anc_chicago", "gender_r", "oq", "bf_1", "bf_2R", "bf_3", "bf_5R", "bf_6", "bf_7R", "bf_8", "bf_10R", "bf_11", "bf_12R", "soc_d_1", "soc_d_2", "soc_d_3R", "soc_d_4", "soc_d_5R", "soc_d_6R", "soc_d_7", "soc_d_8", "soc_d_9R", "soc_d_10R", "soc_d_11R", "soc_d_12R", "soc_d_13", "soc_d_14R", "soc_d_15R", "soc_d_16", "soc_d_17", "soc_d_18", "soc_d_19R", "soc_d_20", "soc_d_21", "soc_d_22R", "soc_d_23R", "soc_d_24", "soc_d_25", "soc_d_26", "soc_d_27", "soc_d_28R", "soc_d_29", "soc_d_30R", "soc_d_31", "soc_d_32R", "soc_d_33", "cr_1R", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7", "everesthigh", "chicagohigh", "babieshigh", "everestlow", "chicagolow", "babieslow", "d1", "d2.sq001", "d2.sq002", "d3.sq001", "d3.sq002", "d3.sq003", "gender", "age", "end", "return", "lastpage", "random", "cb", "ca", "mc_1", "mc_2", "mc_3", "mc_4", "Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness to experience", "Social Desirability", "Careless responses"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "V1",
      "@type": "propertyValue"
    },
    {
      "name": "id",
      "description": "ID variable from raw data",
      "@type": "propertyValue"
    },
    {
      "name": "consent",
      "description": "Variables cb and ca combined in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "cond_anc",
      "description": "Anchoring condition: high and low",
      "@type": "propertyValue"
    },
    {
      "name": "refused",
      "description": "Refusal to participate, one participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. This participant was in the 'no data sharing' condition.",
      "@type": "propertyValue"
    },
    {
      "name": "remember",
      "description": "Mutated variable from consent and mc_3: Does the participant remember the correct data sharing policy?",
      "value": "0. No or wrong memory,\n1. correct memory",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "anc_baby",
      "description": "Aggregated anchoring response, combining variables babieshigh and babieslow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "anc_everest",
      "description": "Aggregated anchoring response, combining variables everesthigh and everestlow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "anc_chicago",
      "description": "Aggregated anchoring response, combining variables chicagohigh and chicagolow in one variable",
      "@type": "propertyValue"
    },
    {
      "name": "gender_r",
      "description": "Gender variable cleaned for grammar, language variations and orthography",
      "@type": "propertyValue"
    },
    {
      "name": "oq",
      "@type": "propertyValue"
    },
    {
      "name": "bf_1",
      "description": "TIPI item 1, Extraversion: I see myself as extraverted, enthousiastic.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_2R",
      "description": "TIPI item 2, Agreeableness: I see myself as critical, quarrelsome.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_3",
      "description": "TIPI item 3, Conscientiousness: I see myself as dependable, self-disciplined.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_5R",
      "description": "TIPI item 4, Neuroticsm: I see myself as anxious, easily upset.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_6",
      "description": "TIPI item 5, Openness to experience: I see myself as open to new experiences, complex.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_7R",
      "description": "TIPI item 6, Extraversion: I see myself as reserved, quiet.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_8",
      "description": "TIPI item 7, Agreeableness: I see myself as sympathetic, warm.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_10R",
      "description": "TIPI item 8, Conscientiousness: I see myself as disorganized, careless.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_11",
      "description": "TIPI item 9, Neuroticsm: I see myself as calm, emotionally stable.",
      "value": "1. Disagree strongly,\n2. Disagree moderately,\n3. Disagree a little,\n4. Neither agree nor disagree,\n5. Agree a little,\n6. Agree moderately,\n7. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_12R",
      "description": "TIPI item 10, Openness to experience: I see myself as conventional, uncreative.",
      "value": "7. Disagree strongly,\n6. Disagree moderately,\n5. Disagree a little,\n4. Neither agree nor disagree,\n3. Agree a little,\n2. Agree moderately,\n1. Agree strongly",
      "maxValue": 7,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_1",
      "description": "Social desirability questionnaire item 1: Before voting I thoroughly investigate the qualifications of all the candidates.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_2",
      "description": "Social desirability questionnaire item 2: I never hesitate to go out of my way to help someone in trouble.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_3R",
      "description": "Social desirability questionnaire item 3: It is sometimes hard for me to go on with my work if I am not encouraged.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_4",
      "description": "Social desirability questionnaire item 4: I have never intensely disliked anyone.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_5R",
      "description": "Social desirability questionnaire item 5: On occasion I have had doubts about my ability to succeed in life.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_6R",
      "description": "Social desirability questionnaire item 6: I sometimes feel resentful when I don't get my way.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_7",
      "description": "Social desirability questionnaire item 7: I am always careful about my manner of dress.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_8",
      "description": "Social desirability questionnaire item 8: My table manners at home are as good as when I eat out in a restaurant.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_9R",
      "description": "Social desirability questionnaire item 9: If I could get into a movie without paying and be sure I was not seen I would probably do it.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_10R",
      "description": "Social desirability questionnaire item 10: On a few occasions, I have given up doing something because I thought too little of my ability.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_11R",
      "description": "Social desirability questionnaire item 11: I like to gossip at times.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_12R",
      "description": "Social desirability questionnaire item 12: There have been times when I felt like rebelling against people in authority even though I knew they were right.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_13",
      "description": "Social desirability questionnaire item 13: No matter who I'm talking to, I'm always a good listener.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_14R",
      "description": "Social desirability questionnaire item 14: I can remember 'playing sick' to get out of something.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_15R",
      "description": "Social desirability questionnaire item 15: There have been occasions when I took advantage of someone.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_16",
      "description": "Social desirability questionnaire item 16: I'm always willing to admit it when I make a mistake.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_17",
      "description": "Social desirability questionnaire item 17: I always try to practice what I preach.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_18",
      "description": "Social desirability questionnaire item 18: I don't find it particularly difficult to get along with loud mouthed, obnoxious people.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_19R",
      "description": "Social desirability questionnaire item 19: I sometimes try to get even rather than forgive and forget.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_20",
      "description": "Social desirability questionnaire item 20: When I don't know something I don't at all mind admitting it.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_21",
      "description": "Social desirability questionnaire item 21: I am always courteous, even to people who are disagreeable.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_22R",
      "description": "Social desirability questionnaire item 22: At times I have really insisted on having things my own way.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_23R",
      "description": "Social desirability questionnaire item 23: There have been occasions when I felt like smashing things.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_24",
      "description": "Social desirability questionnaire item 24: I would never think of letting someone else be punished for my wrong- doings.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_25",
      "description": "Social desirability questionnaire item 25: I never resent being asked to return a favor.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_26",
      "description": "Social desirability questionnaire item 26: I have never been irked when people expressed ideas very different from my own.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_27",
      "description": "Social desirability questionnaire item 27: I never make a long trip without checking the safety of my car.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_28R",
      "description": "Social desirability questionnaire item 28: There have been times when I was quite jealous of the good fortune of others.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_29",
      "description": "Social desirability questionnaire item 29: I have almost never felt the urge to tell someone off.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_30R",
      "description": "Social desirability questionnaire item 30: I am sometimes irritated by people who ask favors of me. ",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_31",
      "description": "Social desirability questionnaire item 31: I have never felt that I was punished without cause.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_32R",
      "description": "Social desirability questionnaire item 32: I sometimes think when people have a misfortune they only got what they deserved.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_33",
      "description": "Social desirability questionnaire item 33: I have never deliberately said something that hurt someone's feelings.",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_1R",
      "description": "Careless response item 1: I am using an electronic device at this moment.",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_2",
      "description": "Careless response item 2: I turn into a leprechaun at night.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_3",
      "description": "Careless response item 3: All my friends are aliens.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_4",
      "description": "Careless response item 4: All my friends say I would make a great poodle.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_5",
      "description": "Careless response item 5: I eat cement occasionally.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_6",
      "description": "Careless response item 6: I can teleport across time and space.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "cr_7",
      "description": "Careless response item 7: I will be punished for meeting the requirements of my job.",
      "value": "0. Yes,\n1. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "everesthigh",
      "description": "Anchoring paradigm, high anchor: Height of Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "chicagohigh",
      "description": "Anchoring paradigm, high anchor: Population of Chicago",
      "@type": "propertyValue"
    },
    {
      "name": "babieshigh",
      "description": "Anchoring paradigm, high anchor: Babies born each day",
      "@type": "propertyValue"
    },
    {
      "name": "everestlow",
      "description": "Anchoring paradigm, low anchor: Height of Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "chicagolow",
      "description": "Anchoring paradigm, low anchor: Population of Chicago",
      "@type": "propertyValue"
    },
    {
      "name": "babieslow",
      "description": "Anchoring paradgim, low anchor: Babies born each day",
      "@type": "propertyValue"
    },
    {
      "name": "d1",
      "description": "NOT USED control question: memory of consent, not used: Think back to the beginning of this study. Do you remember clicking through a consent form, and the information it contained?",
      "@type": "propertyValue"
    },
    {
      "name": "d2.sq001",
      "description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : Yes, I remember",
      "@type": "propertyValue"
    },
    {
      "name": "d2.sq002",
      "description": "NOT USED Answer option to control question d2: 'Do you remember if the consent form dealt with making your anonymous data accessible to others on osf.io?' : No, I don't remember",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq001",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': Yes\n",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq002",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': No",
      "@type": "propertyValue"
    },
    {
      "name": "d3.sq003",
      "description": "NOT USED Answer option to control question d2: 'Will your anonymously collected data for this study be shared on osf.io so it is accessible to others?': I don't remember",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Gender: open-entry self-report",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age categories",
      "@type": "propertyValue"
    },
    {
      "name": "end",
      "@type": "propertyValue"
    },
    {
      "name": "return",
      "@type": "propertyValue"
    },
    {
      "name": "lastpage",
      "description": "Last page completed by the participant, page 12 and 13 are considered as full participation",
      "@type": "propertyValue"
    },
    {
      "name": "random",
      "description": "Randomly attributed study condition. 1 & 2 = not shared, 3 & 4 = shared, 1 & 3 = high anchor in anchoring paradigm, 2 & 4 = low anchor.",
      "@type": "propertyValue"
    },
    {
      "name": "cb",
      "description": "No data sharing policy consent presented. One participant clicked on 'I disagree' but contacted the first author by email to indicate that they had 'a bug' and was unable to complete the questionnaire. See manuscript for details",
      "@type": "propertyValue"
    },
    {
      "name": "ca",
      "description": "Data sharing policy presented",
      "@type": "propertyValue"
    },
    {
      "name": "mc_1",
      "description": "comprehension question consent 1 (distractor): Will this survey take longer than 10 minutes?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_2",
      "description": "comprehension question consent 2 (distractor): Is your data anonymous?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_3",
      "description": "comprehension question/manipulation check: will your data be shared? correct answer depends on condition: Will your data be shared?",
      "@type": "propertyValue"
    },
    {
      "name": "mc_4",
      "description": "comprehension question consent 3 (distractor): Can you stop your participation at any time?",
      "@type": "propertyValue"
    },
    {
      "name": "Extraversion",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Agreeableness",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Conscientiousness",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Neuroticism",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Openness to experience",
      "description": "2 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Social Desirability",
      "description": "33 soc_d items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Careless responses",
      "description": "7 cr items aggregated by rowMeans",
      "@type": "propertyValue"
    }
  ]
}`